Abstract
Initially, electrofacies were introduced to define a set of recorded well log responses in order to characterize and distinguish a bed from the other rock units, as an advancement to the conventional application of well logs. Well logs are continuous records of several physical properties of drilled rocks that can be related to different lithologies by experienced log analysts. This work is time consuming and likely to be imperfect because human analysis is subjective. Thus, any automated classification approach with high promptness and accuracy is very welcome by log analysts. One of the crucial requirements in petroleum engineering is to interpret a bed’s lithology, which can be done by grouping a formation into electrofacies. In the past, geophysical modelling, petro-physical analysis, artificial intelligence and several statistical method approaches have been implemented to interpret lithology. In this research, important well log features are selected by using the Extra Tree Classifier (ETC), and then five individual electrofacies are constructed by using the selected well log features. Finally, a rough set theory (RST)-based whitebox classification approach is proposed to classify the electrofacies by generating decision rules. These rules are later on used to determine the lithology classes and we found that RST is beneficial for performing data mining tasks such as data classification and rule extraction from uncertain and vague well log datasets. A comparison study is also provided, where we use support vector machine (SVM), deep learning based on feedforward multilayer perceptron (MLP) and random forest classifier (RFC) to compare the electrofacies classification accuracy.
Highlights
Lithology refers to the composition or type of rock in the Earth’s subsurface
A number of methods have been used for solving lithology classification and interpretation problems, such as cross plot interpretation and statistical analysis based on histogram plotting [5], support vector machine (SVM) using traditional wireline well logs [6], fuzzy-logics (FL) for association analysis, neural networks and multivariable statistical methodologies [7], artificial intelligence approaches and multivariate statistical analysis [8], hybrid NN methods [9], self organized map (SOM) [10], FL methods [11], artificial neural network (ANN) methods [12,13], lithology classification from seismic tomography [14], multi-agent collaborative learning architecture approaches [15], random forest [16,17], generative adversarial network [18] and multivariate statistical methods [19]
Electrofacies have a very significant bearing on reservoir parameter calculations like lithology, which is shown in the experiment portion
Summary
Lithology refers to the composition or type of rock in the Earth’s subsurface. The term lithology is used as a gross description of a rock layer in the subsurface and uses familiar names, including sandstone, claystone (clay), shale (mudrock), siltstone, and so forth. Electrofacies have a unique set of log responses that can separate the material properties of the rocks and fluids contained in the volume recorded using the well-logging tools. These identified electrofacies can interpret and reflect the lithologic, diagenetic and hydrologic characteristics of an uncored well. The electrofacies can be defined on the basis of standard well-log data, such as neutron porosity, gamma ray, resistivity, bulk density, caliper log, photoelectric effect, and so forth They can often be associated to one or more lithofacies.
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